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Helping robots help us: Using prior information for localization, navigation, and human-robot interaction
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3079-0512
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Maps are often used to provide information and guide people. Emergency maps or floor plans are often displayed on walls and sketch maps can easily be drawn to give directions. However, robots typically assume that no knowledge of the environment is available before exploration even though making use of prior maps could enhance robotic mapping. For example, prior maps can be used to provide map data of places that the robot has not yet seen, to correct errors in robot maps, as well as to transfer information between map representations.

I focus on two types of prior maps representing the walls of an indoor environment: layout maps and sketch maps. I study ways to relate information of sketch or layout maps with an equivalent metric map and study how to use layout maps to improve the robot’s mapping. Compared to metric maps such as sensor-built maps, layout and sketch maps can have local scale errors or miss elements of the environment, which makes matching and aligning such heterogeneous map types a hard problem.

I aim to answer three research questions: how to interpret prior maps by finding meaningful features? How to find correspondences between the features of a prior map and a metric map representing the same environment? How to integrate prior maps in SLAM so that both the prior map and the map built by the robot are improved?

The first contribution of this thesis is an algorithm that can find correspondences between regions of a hand-drawn sketch map and an equivalent metric map and achieves an overall accuracy that is within 10% of that of a human. The second contribution is a method that enables the integration of layout map data in SLAM and corrects errors both in the layout and the sensor map.

These results provide ways to use prior maps with local scale errors and different levels of detail, whether they are close to metric maps, e.g. layout maps, or non-metric maps, e.g. sketch maps. The methods presented in this work were used in field tests with professional fire-fighters for search and rescue applications in low-visibility environments. A novel radar sensor was used to perform SLAM in smoke and, using a layout map as a prior map, users could indicate points of interest to the robot on the layout map, not only during and after exploration, but even before it took place.

Place, publisher, year, edition, pages
Örebro: Örebro University , 2019. , p. 83
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 86
Keywords [en]
graph-based SLAM, prior map, sketch map, emergency map, map matching, graph matching, segmentation, search and rescue
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-75877ISBN: 978-91-7529-299-1 (print)OAI: oai:DiVA.org:oru-75877DiVA, id: diva2:1345270
Public defence
2019-10-29, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2019-08-23 Created: 2019-08-23 Last updated: 2019-10-02Bibliographically approved
List of papers
1. Using sketch-maps for robot navigation: interpretation and matching
Open this publication in new window or tab >>Using sketch-maps for robot navigation: interpretation and matching
2016 (English)In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 252-257Conference paper, Published paper (Refereed)
Abstract [en]

We present a study on sketch-map interpretationand sketch to robot map matching, where maps have nonuniform scale, different shapes or can be incomplete. For humans, sketch-maps are an intuitive way to communicate navigation information, which makes it interesting to use sketch-maps forhuman robot interaction; e.g., in emergency scenarios.

To interpret the sketch-map, we propose to use a Voronoi diagram that is obtained from the distance image on which a thinning parameter is used to remove spurious branches. The diagram is extracted as a graph and an efficient error-tolerant graph matching algorithm is used to find correspondences, while keeping time and memory complexity low.

A comparison against common algorithms for graph extraction shows that our method leads to twice as many good matches. For simple maps, our method gives 95% good matches even for heavily distorted sketches, and for a more complex real-world map, up to 58%. This paper is a first step toward using unconstrained sketch-maps in robot navigation.

Place, publisher, year, edition, pages
New York: Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
sketch, sketch-map, human robot interface, HRI, graph matching
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-53826 (URN)10.1109/SSRR.2016.7784307 (DOI)000391310800039 ()2-s2.0-85009754966 (Scopus ID)978-1-5090-4349-1 (ISBN)
Conference
14th IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR 2016), EPFL, Lausanne, Switzerland, October 23-27, 2016
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBot
Available from: 2016-12-09 Created: 2016-12-07 Last updated: 2019-10-02Bibliographically approved
2. A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation
Open this publication in new window or tab >>A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.

Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values.

We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method.

We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
map segmentation, free space, layout
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68421 (URN)10.1109/ICRA.2018.8461128 (DOI)000446394503114 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBotKnowledge Foundation, 20140220
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-10-10Bibliographically approved
3. URSIM: Unique Regions for Sketch Map Interpretation and Matching
Open this publication in new window or tab >>URSIM: Unique Regions for Sketch Map Interpretation and Matching
2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 2, article id 43Article in journal (Refereed) Published
Abstract [en]

We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human-robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Map matching, sketch, human-robot interaction, interface, graph matching, segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75741 (URN)10.3390/robotics8020043 (DOI)000475325600020 ()2-s2.0-85069975721 (Scopus ID)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-26-2016 732737

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-10-10Bibliographically approved
4. SLAM auto-complete: completing a robot map using an emergency map
Open this publication in new window or tab >>SLAM auto-complete: completing a robot map using an emergency map
2017 (English)In: 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), IEEE conference proceedings, 2017, p. 35-40, article id 8088137Conference paper, Published paper (Refereed)
Abstract [en]

In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots in such scenarios has been restricted by the time needed to explore and build a map. One way to speed up exploration and mapping is to reason about unknown parts of the environment using prior information. While previous research on using external priors for robot mapping mainly focused on accurate maps or aerial images, such data are not always possible to get, especially indoor. We focus on emergency maps as priors for robot mapping since they are easy to get and already extensively used by firemen in rescue missions. However, those maps can be outdated, information might be missing, and the scales of rooms are typically not consistent.

We have developed a formulation of graph-based SLAM that incorporates information from an emergency map. The graph-SLAM is optimized using a combination of robust kernels, fusing the emergency map and the robot map into one map, even when faced with scale inaccuracies and inexact start poses.

We typically have more than 50% of wrong correspondences in the settings studied in this paper, and the method we propose correctly handles them. Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result. The robot can navigate and explore while taking into account places it has not yet seen. We demonstrate this in a test scenario and also show that the emergency map is enhanced by adding information not represented such as closed doors or new walls.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2017
Keywords
SLAM, robotics, graph, graph SLAM, emergency map, rescue, exploration, auto complete, SLAM, robotics, graph, graph SLAM, plan de secours, sauvetage, exploration, auto complete
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-62057 (URN)10.1109/SSRR.2017.8088137 (DOI)000426991900007 ()2-s2.0-85040221684 (Scopus ID)978-1-5386-3923-8 (ISBN)978-1-5386-3924-5 (ISBN)
Conference
15th IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR 2017), ShanghaiTech University, China, October 11-13, 2017
Projects
EU H2020 project SmokeBot (ICT- 23-2014 645101)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-23-2014645101

Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2019-10-02Bibliographically approved
5. The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM
Open this publication in new window or tab >>The Auto-Complete Graph: Merging and Mutual Correction of Sensor and Prior Maps for SLAM
2019 (English)In: Robotics, E-ISSN 2218-6581, Vol. 8, no 2, article id 40Article in journal (Refereed) Published
Abstract [en]

Simultaneous Localization And Mapping (SLAM) usually assumes the robot starts without knowledge of the environment. While prior information, such as emergency maps or layout maps, is often available, integration is not trivial since such maps are often out of date and have uncertainty in local scale. Integration of prior map information is further complicated by sensor noise, drift in the measurements, and incorrect scan registrations in the sensor map. We present the Auto-Complete Graph (ACG), a graph-based SLAM method merging elements of sensor and prior maps into one consistent representation. After optimizing the ACG, the sensor map's errors are corrected thanks to the prior map, while the sensor map corrects the local scale inaccuracies in the prior map. We provide three datasets with associated prior maps: two recorded in campus environments, and one from a fireman training facility. Our method handled up to 40% of noise in odometry, was robust to varying levels of details between the prior and the sensor map, and could correct local scale errors of the prior. In field tests with ACG, users indicated points of interest directly on the prior before exploration. We did not record failures in reaching them.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
SLAM, prior map, emergency map, layout map, graph-based SLAM, navigation, search and rescue
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75742 (URN)10.3390/robotics8020040 (DOI)000475325600017 ()2-s2.0-85069926702 (Scopus ID)
Funder
Knowledge Foundation, 20140220
Note

Funding Agency:

EU  ICT-26-2016 732737  ICT-23-2014 645101

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-10-02Bibliographically approved
6. A comparative analysis of radar and lidar sensing for localization and mapping
Open this publication in new window or tab >>A comparative analysis of radar and lidar sensing for localization and mapping
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping.

We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-76976 (URN)
Conference
9th European Conference on Mobile Robots (ECMR 2019), Prague, Czech Republic, September 4-6, 2019
Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

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